新疆阜康荒地土壤有机质高光谱特征及其反演模型研究
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摘要
针对干旱区荒地土壤贫瘠且有机质含量少,难以快速、准确测定的问题,以阜康中部荒地土壤为研究对象,对64个样点野外光谱进行测定和室内土壤样品农化分析,在原始反射率(R)基础上,利用ENVI5.1软件提取光谱反射率一阶微分(R')、倒数的对数(lg(1/A))、倒数的对数一阶微分(lg(1/A)')、去包络线(CR)等4种光谱反射率,分析了5种光谱反射率的变换形式与土壤有机质含量的相关性,基于全波段(450～2 350 nm)和显著性波段(相关系数通过P=0.01水平检验),利用偏最小二乘法回归(PLSR)建立土壤有机质含量的高光谱预测模型。结果表明:(1)对不同有机质含量的土壤光谱去包络线后,光谱曲线吸收特征差异更加显著,且土壤有机质含量越多,土壤光谱反射率越低。(2)土壤反射率经过数学变换后提高了与有机质含量的相关系数。(3)在全波段的PLSR中,CR、R'和lg(1/R)'模型的RPD均大于2.0,表明预测能力极好。其中以CR的预测精度最为突出,其模型R2和RMSE分别为0.79、4.12,RPD为2.18。在显著性波段的PLSR中,虽然R'和CR的模型RPD均大于2.0,可以准确预测有机质含量,但CR的R2,RPD更高;基于全波段PLSR模型精度均略优于显著性波段,但其使用数据量大,增加了计算量。同时,其CR模型的RPD仅比显著性波段模型的高0.03。因此,选择显著性波段CR模型作为估测该荒地土壤有机质含量的模型更为简洁、科学、可行。
It is difficult to determine concentration of soil nutrition and organic matter accurately and quickly for poverty of soil with less organic matter in waste land,arid region. Concentration of organic matter and field spectra of 64 soil samples from barren land in central Fukang,were analyzed. Based on the original reflectance( R),the software ENVI5.1 was used to extract the first derivative reflectance( R'),logarithmic reciprocal( lg( 1/A)),the logarithm of reciprocal derivative( lg( 1/A) '),to envelope( CR) and other 4 kinds of spectral reflectance. The correlation between the 5 spectral reflectance transformation form and concentration of soil organic matter was analyzed. Based on the full band( 450 ～ 2 350 nm) and significant band( correlation coefficient by P = 0. 01 level test),prediction models of soil organic matter conenttation were regressed by partial least squares regression( PLSR) hyperspectral. The results showed that:( 1) The spectral curve absorption characteristics of soil with different organic matter concentration were more significant. Concentration of soil organic matter,correlated inversely to the spectral reflectance of soil.( 2) The correlation coefficient between soil reflectance and the concentration of organic matter was enhanced by mathematical transformation.( 3) In the whole band of PLSR,the RPD of CR,R' and lg( 1/R) model was greater than 2,which indicated that the prediction ability was excellent. Among them,the prediction accuracy of CR was the most prominent,and the model R~2 and RMSE were 0.79 and 4.12,respectively,and RPD was 2.18. In a significant band of PLSR,while RPD R' and CR model were greater than 2,could accurately predict the concentation of organic matter. However,R~2 and RPD of the CR were higher precision,full spectrum PLSR model was slightly better than significant band based and increased the amount of calculation for its use of large amount of data. At the same time,the RPD of the CR model was only 0.03 higher than which of the significant band model. Therefore,it is more concise,scientific and feasible to choose the significant band CR model as a model to estimate the content of organic matter in the uncultivated land.
It is difficult to determine concentration of soil nutrition and organic matter accurately and quickly for poverty of soil with less organic matter in waste land,arid region. Concentration of organic matter and field spectra of 64 soil samples from barren land in central Fukang,were analyzed. Based on the original reflectance( R),the software ENVI5.1 was used to extract the first derivative reflectance( R'),logarithmic reciprocal( lg( 1/A)),the logarithm of reciprocal derivative( lg( 1/A) '),to envelope( CR) and other 4 kinds of spectral reflectance. The correlation between the 5 spectral reflectance transformation form and concentration of soil organic matter was analyzed. Based on the full band( 450 ～ 2 350 nm) and significant band( correlation coefficient by P = 0. 01 level test),prediction models of soil organic matter conenttation were regressed by partial least squares regression( PLSR) hyperspectral. The results showed that:( 1) The spectral curve absorption characteristics of soil with different organic matter concentration were more significant. Concentration of soil organic matter,correlated inversely to the spectral reflectance of soil.( 2) The correlation coefficient between soil reflectance and the concentration of organic matter was enhanced by mathematical transformation.( 3) In the whole band of PLSR,the RPD of CR,R' and lg( 1/R) model was greater than 2,which indicated that the prediction ability was excellent. Among them,the prediction accuracy of CR was the most prominent,and the model R~2 and RMSE were 0.79 and 4.12,respectively,and RPD was 2.18. In a significant band of PLSR,while RPD R' and CR model were greater than 2,could accurately predict the concentation of organic matter. However,R~2 and RPD of the CR were higher precision,full spectrum PLSR model was slightly better than significant band based and increased the amount of calculation for its use of large amount of data. At the same time,the RPD of the CR model was only 0.03 higher than which of the significant band model. Therefore,it is more concise,scientific and feasible to choose the significant band CR model as a model to estimate the content of organic matter in the uncultivated land.
引文
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[7]谭琨,张倩倩,曹茜,等.基于粒子群优化支持向量机的矿区土壤有机质含量高光谱反演[J].地球科学:中国地质大学学报,2015,(8):1339-1345.
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[9]王一丁,赵铭钦,刘鹏飞,等.基于高光谱分析的植烟土壤有机质和全氮含量预测研究[J].中国烟草学报,2016,(03):44-51.
[10]于雷,洪永胜,耿雷,等.基于偏最小二乘回归的土壤有机质含量高光谱估算[J].农业工程学报,2015,31(14):103-109.
[11]南锋,朱洪芬,毕如田.黄土高原煤矿区复垦农田土壤有机质含量的高光谱预测[J].中国农业科学,2016,49(11):2126-2135.
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[13]李颉,张小超,苑严伟,等.北京典型耕作土壤养分的近红外光谱分析[J].农业工程学报,2012,28(02):176-179.
[14]李荣荣,熊黑钢,段鹏程,等.干旱区平原水库下游盐渍化土壤光谱时空分布特征分析[J].土壤通报,2016,47(3):532-536.
[15]何挺,王静,林宗坚,等.土壤有机质光谱特征研究[J].武汉大学学报(信息科学版),2006,(11):975-979.
[16]郑光辉,焦彩霞,赏刚,等.土壤全氮反射光谱估算机理研究[J].光谱学与光谱分析,2016,(10):3222-3225.
[17]韩云霞,李民赞,李道亮.基于光谱学与遥感技术的矿区废弃地土壤特性参数分析[J].吉林大学学报,2009,39(1):254-257.
[18]郭熙,叶英聪,谢碧裕,等.南方丘陵稻田土碱解氮高光谱特征及反演模型研究[J].国土资源遥感,2015,27(2):94-99.
[19] Viscarra R V,Mc Glyn R N,Mc Bratney A B. Determing the composition of mineral-organic mixes using UV-vis-NIR diffuse Reflectance spectroscopy[J].Geoderma,2006,137(1/2):70-82.
[20]全国第二次土壤普查养分分级标准[EB/OL].[2012-05-10],http://wenku.baidu.com/view/1242f6ef856a2561252d36f6e.html.
[21]官晓,周萍,陈圣波.基于地面实测光谱的土壤有机质含量预测[J].国土资源遥感,2014,26(2):105-111.
[22]季耿善,徐彬彬.土壤黏土矿物反射特性及其在土壤学上的应用[J].土壤学报,1987,24(1):67-76.
[23]彭杰,周清,张杨珠,等.有机质对土壤光谱特性的影响研究[J].土壤学报,2013,50(3):517-524.
[24]金慧凝,张柏,王宗明,等.基于反射光谱特征的土壤盐碱化评价[J].红外与毫米波学报,2008,27(2):138-142.
[25]纪文君,史舟,周清,等.几种不同类型土壤的VIS-NIR光谱特性及有机质响应波段[J].红外与毫米波学报,2012,(03):277-282.
[26]郭斗斗,黄绍敏,张水清,等.多种潮土有机质高光谱预测模型的对比分析[J].农业工程学报,2014,30(21):192-200.
[27]丁希斌,刘飞,张初,等.基于高光谱成像技术的油菜叶片SPAD值检测[J].光谱学与光谱分析,2015,35(2):486-491.
[28]纪文君,史舟,周清,等.几种不同类型土壤的VIS-NIR光谱特性及有机质响应波段[J].红外与毫米波学报,2012,31(3):277-282.
[2] Henderson T L,Umgardner M F,Ranzeier D P,et al.Dimensional reflectance analysis of soil organic matter[J].Soil Sci.Soc.Am.J,1992,56:865-872.
[3] Al-Abbas A H,Swain P H,Baumgarder M F.Relating organic matter and clay content to the multi-spectral radiance of soils[J]. Soil Science,1972,114(6):477-485.
[4] Baumgardner M F,Silva L F,Biehl L L,et al.Reflectance proper-ties of soils[J].Advances in Agronomy,1985,38:1-44.
[5] Hummel J W,Sudduth K A,Hollinger S E.Soil moisture and organic matter Prediction of surface and subsurface soils using an NIR soi1senso[J]. Com Puters and Electronics in Agriculture,2001,32(2):149-165.
[6]徐明星,周生路,丁卫,等.苏北沿海滩涂地区土壤有机质含量的高光谱预测[J].农业工程学报,2011,27(2):219-223.
[7]谭琨,张倩倩,曹茜,等.基于粒子群优化支持向量机的矿区土壤有机质含量高光谱反演[J].地球科学:中国地质大学学报,2015,(8):1339-1345.
[8]叶勤,姜雪芹,李西灿,等.基于高光谱数据的土壤有机质含量反演模型比较[J].农业机械学报,2017,(03):164-172.
[9]王一丁,赵铭钦,刘鹏飞,等.基于高光谱分析的植烟土壤有机质和全氮含量预测研究[J].中国烟草学报,2016,(03):44-51.
[10]于雷,洪永胜,耿雷,等.基于偏最小二乘回归的土壤有机质含量高光谱估算[J].农业工程学报,2015,31(14):103-109.
[11]南锋,朱洪芬,毕如田.黄土高原煤矿区复垦农田土壤有机质含量的高光谱预测[J].中国农业科学,2016,49(11):2126-2135.
[12]朱登胜,吴迪,宋海燕,等.应用近红外光谱法测定土壤的有机质和p H值[J].农业工程学报,2008,24(6):196-199.
[13]李颉,张小超,苑严伟,等.北京典型耕作土壤养分的近红外光谱分析[J].农业工程学报,2012,28(02):176-179.
[14]李荣荣,熊黑钢,段鹏程,等.干旱区平原水库下游盐渍化土壤光谱时空分布特征分析[J].土壤通报,2016,47(3):532-536.
[15]何挺,王静,林宗坚,等.土壤有机质光谱特征研究[J].武汉大学学报(信息科学版),2006,(11):975-979.
[16]郑光辉,焦彩霞,赏刚,等.土壤全氮反射光谱估算机理研究[J].光谱学与光谱分析,2016,(10):3222-3225.
[17]韩云霞,李民赞,李道亮.基于光谱学与遥感技术的矿区废弃地土壤特性参数分析[J].吉林大学学报,2009,39(1):254-257.
[18]郭熙,叶英聪,谢碧裕,等.南方丘陵稻田土碱解氮高光谱特征及反演模型研究[J].国土资源遥感,2015,27(2):94-99.
[19] Viscarra R V,Mc Glyn R N,Mc Bratney A B. Determing the composition of mineral-organic mixes using UV-vis-NIR diffuse Reflectance spectroscopy[J].Geoderma,2006,137(1/2):70-82.
[20]全国第二次土壤普查养分分级标准[EB/OL].[2012-05-10],http://wenku.baidu.com/view/1242f6ef856a2561252d36f6e.html.
[21]官晓,周萍,陈圣波.基于地面实测光谱的土壤有机质含量预测[J].国土资源遥感,2014,26(2):105-111.
[22]季耿善,徐彬彬.土壤黏土矿物反射特性及其在土壤学上的应用[J].土壤学报,1987,24(1):67-76.
[23]彭杰,周清,张杨珠,等.有机质对土壤光谱特性的影响研究[J].土壤学报,2013,50(3):517-524.
[24]金慧凝,张柏,王宗明,等.基于反射光谱特征的土壤盐碱化评价[J].红外与毫米波学报,2008,27(2):138-142.
[25]纪文君,史舟,周清,等.几种不同类型土壤的VIS-NIR光谱特性及有机质响应波段[J].红外与毫米波学报,2012,(03):277-282.
[26]郭斗斗,黄绍敏,张水清,等.多种潮土有机质高光谱预测模型的对比分析[J].农业工程学报,2014,30(21):192-200.
[27]丁希斌,刘飞,张初,等.基于高光谱成像技术的油菜叶片SPAD值检测[J].光谱学与光谱分析,2015,35(2):486-491.
[28]纪文君,史舟,周清,等.几种不同类型土壤的VIS-NIR光谱特性及有机质响应波段[J].红外与毫米波学报,2012,31(3):277-282.
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